The cardiovascular system generates a rich assortment of normal and abnormal physiological sounds. The abnormal sounds are usually indicative of one or more cardiovascular diseases. A mode of diagnosing cardiovascular diseases using cardiovascular sounds is of great benefit because sound detection poses minimal risk to a subject being diagnosed. Traditional clinical auscultation, of course, has been used in this manner, but this mode is limited in that it cannot detect sounds that are outside the normal human auditory range or those that are obscured by normal physiological sounds. Indeed, several sounds indicative of cardiovascular disease fall into these categories.
One general methodology that has been proposed to overcome this difficulty is to use a digital stethoscope device to record cardiovascular sounds and then to apply signal-processing techniques to identify the audible and sub-audible features of interest present in the recorded cardiovascular sound signals. These features of interest, in turn, can either directly or indirectly serve as discriminants between the normal and abnormal cardiovascular sounds, and hence they can indicate or contraindicate cardiovascular diseases. For example, discriminating between normal and abnormal cardiovascular sounds is often performed by selecting features of interest from a mathematical model of the cardiovascular sound signals.
Unfortunately, the discriminatory power of such prior techniques has proved weaker than desired for some applications. Moreover, better capability for identifying predictors of cardiovascular disease and/or determining a probability of cardiovascular disease is desired.